Aeyeksy
Enabling Smart Observing Services for Casinos
YEAR
2025
Project Length
5 Month
Platform
Desktop Application
As the product designer of Aeyesky, I redesigned the workflow by balancing the AI detection and human employee surveillance, prioritizing on delivering accessible user experiences.
My Role
Product Designer
Timeline
Oct 2025 - Now
Team Member
1 Product Manager
1 Product Designer (Myself)
1 Algorithm Expert
2 Developers
Tools
Problem
A Reliable Smart Surveillance System with AI Data
While transitioning from manual video review to AI-driven surveillance promises operational efficiency, it introduces a critical trust gap for casino staff. Although the system automates data collection and analysis for table games, AI leads to inevitable inaccuracies that traditional teams are hard to manage.
Black Box Uncertainty
AI detection often lacks transparency, making it difficult for users to understand why a specific fault was flagged and how it could be solve. Without clear communication of AI, inaccuracies can compromise the system's perceived reliability and analytical credibility.
The Cognitive Barrier
Casino officers typically lack prior experience in AI workflows or dataset cleanup. Moving from intuitive manual observation to a data-driven interface creates a steep learning curve, requiring a design that empowers non-technical users to verify and correct AI outputs seamlessly.
Research & Insight
Understanding Our Users
There are 3 types of users that current Aeyesky's SaaS targeted for.
01
Surveillance Officer
Monitor gaming, review footage and ensure regulatory compliance.
02
Game Protection Analyst
Protect game and identify procedural errors through data and visual analysis.
03
Manager
Oversee the entire security operation
Misunderstanding of Raw Video
01.
While users rely on real-time previews to monitor table status, video thumbnails alone prove insufficient for rapid decision-making, including identifying dealer faults or performance trends.
The Calibration Training Barrier
01.
While users rely on real-time previews to monitor table status, video thumbnails alone prove insufficient for rapid decision-making, including identifying dealer faults or performance trends.
The Fragmented Work Loop
02. 03
The current communication chain between surveillance officers, managers, and analysts creates a operational disconnect because analysts are often brought in late to categorize cases that have already been flagged, lack of real-time table awareness.
The Reporting Disconnect
01. 02. 03.
While analysts and surveillance officers are tasked with reporting significant faults and performance trends to management, a gap exists in the documentation workflow. The current process makes it difficult to isolate specific time periods or translate complex AI data into accessible, high-level summaries for stakeholders.
Solution
Transforming a complex casino surveillance system into a automated process.
User Goal
Business Goal
TASK 1 - Gain Feedback from Users
Streamline the Auto-detection for Incidents
Painpoint
Design Strategy
Onboarding is hard.
Have suggested steps when the user prompt the edit the calibration page
Officers are unable to recognize the errors without previous context.
Add alert tooltips when the area is not being labeled
Set a toolbar identify all the possible actions with annotations.
Show tooltips to prompt for the possible action within the user flow.
Final Design
TASK 2 - Trade off with Dev Team
Review Dealer Fault Incidents
Painpoint
Design Strategy
The categorization for cases is repetitive and unclear.
Bound doable actions with specific categories
Provide an entry for reporting errors
Supervisors are not able to have a full picture of the incidents
Contain a visualized dashboard for an overview. Different types of incidents need their own individual report
Initial Design
Final Design
Reflection
Design with AI Automation
To support officers without AI or data-cleaning experience, the system provides interpretable reasoning rather than raw model output. Each AI-annotated result is accompanied by visual cues and explanations that indicate what was detected and why it was flagged.
Meanwhile, Recognizing that human judgment remains essential, the system provides simple correction and confirmation actions
On-boarding is Important
In the traditional casino environment, the detection is happened when groups of people manually checked camera for possible dealer fault. Most of them don’t have any prior experience in dataset cleanup or AI use. So there should be instruction around the screen when the users first open an interface.
